quantum-simulation-lab 1.0.4

A test-driven tensor network playground for finite-size DMRG and MPS/MPO algorithms

quantum-simulation-lab

A test-driven tensor network library for finite-size DMRG and MPS/MPO algorithms on 1D quantum lattice models.

Installation · Quickstart · Features · Roadmap · Diary

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Overview

quantum-simulation-lab is a from-scratch Python implementation of the core tensor network stack needed for finite-size DMRG on 1D quantum lattice models. The focus is on correctness and clarity — every algorithm is covered by a dense test suite and cross-validated against iTensor.

This project serves as both a learning vehicle and a research platform, built with the same engineering discipline expected in production scientific software: strict CI, typed APIs, and regression tests at every layer.

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Features

🧱 Core Tensor Network Primitives

• Tensor, Index — numpy-backed tensors with named indices and full linear algebra support
• MPS — matrix product states with product-state, statevector, and qubit-label constructors
• MPO — matrix product operators with identity, dense conversion, and MPS application
• TruncationPolicy — configurable SVD truncation (cutoff, max bond dimension, strict mode)
• Utility routines for expectation values and left/right environments

⚛️ Hamiltonian Builders

• Transverse-field Ising model (TFIM)
• XXZ Heisenberg model — via heisenberg_mpo and XX wrappers
• Random field models — X and Z directions, ZZ+Z coupling

🔬 Finite-Size 2-Site DMRG

• Mixed-canonical MPS, incremental left/right environments
• Two-site effective Hamiltonians with correct gauge fixing per sweep direction
• Converges on TFIM, Heisenberg, random Z/X fields, and ZZ+Z — results match iTensor

🧪 Test Suite

• 315+ pytest tests — unit, integration, dense-reference checks, and DMRG regression tests
• CI runs on every push and pull request via GitHub Actions

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Installation

pip install quantum-simulation-lab

Or from source:

git clone https://github.com/szmbthydmnk/quantum-simulation-lab.git
cd quantum-simulation-lab
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

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Quickstart: Ground State of TFIM with DMRG

import numpy as np
from tensor_network_library.core.env import Environment
from tensor_network_library.core.mps import MPS
from tensor_network_library.core.policy import TruncationPolicy
from tensor_network_library.algorithms.dmrg import finite_dmrg, DMRGConfig
from tensor_network_library.hamiltonian.models import tfim_mpo

L, J, g = 10, 1.0, 1.0

env = Environment.qubit_chain(L=L, chi_max=32)
mpo = tfim_mpo(L=L, J=J, g=g)

rng = np.random.default_rng(0)
psi = rng.standard_normal(2**L) + 1j * rng.standard_normal(2**L)
mps0 = MPS.from_statevector(psi / np.linalg.norm(psi), physical_dims=2, normalize=True)

config = DMRGConfig(max_sweeps=10, energy_tol=1e-10, verbose=True)
result = finite_dmrg(env, mpo, mps0, config)

print(f"Ground-state energy: {result.energies[-1]:.12f}")

See examples/ for convergence plots and random-field model scripts.

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Running Tests

pytest

The CI badge above reflects the current state of the main branch.

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Roadmap

Version	Focus	Status
v1	Finite-size DMRG — qubit and spin-1/2 bosonic chains	✅ Done
v2	TEBD / iTEBD — fermionic and spin-1 environments	🔲 Planned
v3	2D geometries and long-range Hamiltonians	🔲 Planned
v4+	Advanced interfaces (AQA, QML, QEC)	🔲 Exploratory

See ROADMAP.md for detailed per-version task lists and DIARY.md for development notes.

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Lines of code

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